Find research datasets worth reusing

opencc-byMay 2022View details →

Measurement Report: Investigation of pH- and particle size-dependent chemical and optical properties of water-soluble organic carbon: implications for its sources and aging processes

The original dataset of the manuscript: "Measurement Report: Investigation of pH- and particle size-dependent chemical and optical properties of water-soluble organic carbon: implications for its sources and aging processes"

Teaching software processes from different application domains

In a current application development scenario in different environments, technologies and contexts, such as IoT, Blockchain, Machine Learning and Cloud Computing, there is a need for particular solutions for domain-specific software development processes. The proper definition of software processes requires understanding the involved teams and organization’s particularities and specialized technical knowledge in Software Engineering. Although it is an essential part of Software Engineering, many university curricula do not dedicate as much effort to teaching software processes, focusing more on the basic principles of Software Engineering, such as requirements, architecture and programming languages. Another important aspect of software processes is modeling. The modeling of a software process provides a basis for managing, automating and supporting the software processes improvement. In this context, teaching software processes modeling becomes challenging, mainly due to the great emphasis on theory and few practices. This work presents an experience report teaching the definition and modeling of software processes in different domains. We apply in the discipline of software processes a practice for defining and modeling processes in various application domains, such as: IoT, cloud, mobile, critical systems, self-adaptive systems and games. The processes were modeled in the EPF composer tool based on references from the literature for each domain. In the end, we evaluated the process modeling practice with the students. We concluded that the modeling tool and the maturity in the domain are essential for the good performance of the process.

opencc-by-4.0May 2022View details →

Impact of Cloud Process in the Mixing State and Microphysical Properties of Soot Particles: Implications in Light Absorption Enhancement

The data of paper of "Impact of Cloud Process in the Mixing State and Microphysical Properties of Soot Particles: Implications in Light Absorption Enhancement"

Model output dataset used in "Sensitivity of Heavy Convective Precipitation Simulations to Changes in Land-atmosphere Exchange Processes over China"

This dataset accompanies the paper by Zhang et al. "Sensitivity of Heavy Convective Precipitation Simulations to Changes in Land-atmosphere Exchange Processes over China". Three heavy precipitation events were modeled using the WRF v3.9 model: (1) The_21_July_Beijing_Rainstorm_Simulation (2) The_30_July_Ningxia_rainstorm_Simulation (3) The_19_June_Jiangxi_rainstorm_Simulation Furthermore, three cases were designed for each heavy precipitation event: (1) control experiment (DEFAULT), using the default M-O option (Czil ~ 0); (2) constant Czil (CZIL0.01, CZIL0.05, CZIL0.1, CZIL0.3, CZIL0.5 and CZIL0.8), with Czil values of 0.01, 0.05, 0.1, 0.3, 0.5, and 0.8; (3) a dynamic canopy-height dependent Czil (NEWCZIL). Plain Language Summary for this paper: Over recent decades, the frequent occurrence of heavy precipitation events has caused devastating ecological and socioeconomic impacts, such as agriculture losses, infrastructure damage, and casualties. High-resolution atmospheric modeling at a convection-permitting grid spacing (≤4 km) provides valuable applications for predicting heavy precipitation. Precipitation can be strongly affected by the energy and moisture exchanges between land surface and atmosphere. However, the representation of land-atmosphere interactions in atmospheric models and the responses of precipitation to land-atmosphere exchange efficiency remain great uncertainties. This study performed 3-km high-resolution atmospheric modeling with a dynamic vegetation-type-dependent land-atmosphere exchange scheme for three typical heavy precipitation events that occurred over areas with different dominant land-cover types. The results showed that land-atmosphere exchange efficiency mainly affected the precipitation intensity as well as the onset and peak time of precipitation. The dynamic exchange scheme modifies the efficiency of land-atmosphere exchanges to match local land cover conditions and could reproduce well the field observations, especially the intensity and location of the heaviest rainfall which usually serve as the most concerned variable in a major rainstorm event. Our findings show that the dynamical scheme could help achieve more accurate precipitation simulations.

Dataset: Debunking the Stereotypical Ontology Development Process

Corresponding Dataset to given Paper: Ontologies facilitate meaning between human and computational actors. On the one hand, the underlying technology can be considered mature. It has a standardized language, established tools for editing and sharing, and broad adoption in practice and research. On the other hand, we still know little about how these artifacts evolve over their lifetime, even though knowledge of the development process could guide quality control. It would enable us to give knowledge engineers better modeling or selection guidelines. This paper examines the evolution of computational ontologies using ontology metrics. First, we gathered hypotheses on the ontology development processes. We assume that groups of ontologies follow a similar development pattern and that a stereotypical development process exists. Afterward, these hypotheses are tested against historical metric data from 7053 versions from 69 dormant ontologies. We will show that ontology development processes are highly heterogeneous. While the made hypotheses are partly true for a slight majority of ontologies, concluding the bigger picture of ontology development down to the individual ontologies is mostly not possible.

Processed PyTorch Geometric dataset (out of date)

PyTorch dataset processed from<a href="https://zenodo.org/record/7064665"> </a><a href="https://zenodo.org/record/6908291">https://zenodo.org/record/6908291</a>

Processed PyTorch Geometric dataset

PyTorch dataset processed from https://zenodo.org/record/6911930

Boundary Data set : Land Cover Classification with Gaussian Processes using spatio-spectro-temporal features

Boundary data set used to evaluate the continuity predictions for different models in boundary zones. It is composed of labelled and unlabelled pixels for a boundary size of 100m and 200m. For further details see section VI-A-1 of the pre-print article "Land Cover Classification with Gaussian Processes using spatio-spectro-temporal features ". This article is available <a href="https://hal.archives-ouvertes.fr/hal-03781332">here</a>. To compute the predictions in the boundary zones with different models (GP, RF, MLP, LTAE), the code is available in the <a href="https://gitlab.cesbio.omp.eu/belletv/land_cover_southfrance_gp">open source repository</a>.

opencc-by-4.0Oct 2021View details →

Table S1 Full factorial combination of laser process parameters and experimental results.

This dataset is experimental results from a research topic of A robust artificial intelligence-based method for predicting and optimizing cut quality of curved laser cutting of electrical steel sheet.

opencc-by-4.0Nov 2021View details →

Network Theme: Continuous blood processing for patient specific therapies - Dr Robert Barker (University of Kent)

This video is the third talk from our Future Blood Testing Network Plus Launch that took place on the 23/11/2021. Network Theme: Continuous blood processing for patient specific therapies - Dr Robert Barker (University of Kent) Bio: Dr Robert Barker is an Associate Professor in Chemistry in the School of Physical Sciences, School Director of Public Engagement, project lead for the ‘Future Human’ Signature Research, University of Kent (UoK). He is a Royal Society Industrial Fellow and the PI of the trans-disciplinary EPSRC project Optimising Me Manufacturing Systems [EP/R022534/1], exploring continuous blood processing for healthcare and developing a healthcare micro-factory that provides on-the-body manufacturing of therapeutics, continuously and in response to the body’s needs. His work focuses on the whole blood processing and the development of a manufacturing system for T-cell immunotherapies which present a cure for some of the most aggressive forms of cancer (acute lymphoblastic leukaemia and chronic lymphoblastic leukaemia). His research is at the interface of industry and the NHS to meet their needs for continuous blood monitoring and to help the shift away from the current one-size-fits-all approach to deliver patient-specific therapies. Further details on this event can be found at: https://futurebloodtesting.org/event/23-11-21-future-blood-testing-network-launch/ This video is an output from the Future Blood Testing Network which is funded by EPSRC under Grant Number EP/W000652/1 YouTube Link: https://youtu.be/T4UVEOM1cZU

Processed UAV orthomosaics and DEMs of Fjallsjökull, southeast Iceland (1 of 2)

Orthomosaics and DEMs of the lower ice surface of Fjallsjökull, southeast Iceland, produced from high resolution UAV surveys undertaken in July and September 2019, and July 2021. This data was produced as part of my PhD research, and therefore, forms a significant component of my final PhD thesis. All files are in UTM Zone 28N. The resolution of the uploaded orthomosaics and DEMs are given below.  2019 Orthomosaics and DEMs exported at 0.03 m and 0.07 m resolution, respectively. 2021 Orthomosaics and DEMs exported at 0.02 m and 0.05 m resolution, respectively.  Please note this is dataset 1 out of 2 (due to the 50 GB limit of file uploads). The remaining orthomosaics from 2021 have been uploaded to dataset 2 of 2 (DOI: 10.5281/zenodo.7111111).  Consequently this dataset only includes the following: 1) All orthomosaics and DEMs from 2019, produced from the following dates: 5th, 6th, 7th and 9th of July; 19th, 20th and 21st of September. 2) All DEMs from 2021, produced from the following dates: 4th, 6th, 7th, 8th, 9th, 10th, 11th, 12th and 15th of July. 3) Three orthomosaics from July 2021, produced from the following dates: 4th, 6th and 7th of July.

Outputs of Numerical experiments verifying the diverse interannual variability of Asian summer monsoon onset process

Introduction We designed two sets of numerical experiments using the Community Earth System Model (CESM 1.2.2) released by NCAR to verify the SSTAs' effect on the interannual modes of ASMOP in April and May. In the atmospheric general circulation model (AGCM) experiments, we solely forced the Community Atmosphere Model version 5.3 (CAM5.3), the atmospheric component of the CESM 1.2.2, by the specific SSTAs in April–May. The AGCM control run included a 40-yr integration forced by the climatological sea surface temperature (SST) with the annual cycle. We attached the specific SSTAs to the climatological SST in each sensitivity experiment and integrated them for 40 years. The ensemble results in the last 30 years were used for analysis. By contrast, in the PACEMAKER experiments, we first integrated the CESM 1.2.2 for 200 years as a control run to exclude the evident climate shift. Afterward, the sensitivity experiments were conducted on the last 100-yr outputs of the control run, in which we nudged the April–May SSTAs in the specific domain but left the rest of the model's coupled climate system free to evolve. The last 30 members were ensembled for analysis in each PACEMAKER experiment.  Numerical Experiment Design In the AGCM category, we forced CAM 5.3 by warm SSTAs in the southwestern Indian Ocean (20°S-0°, 40°-80°E) in the ASMOP-PC2 experiment but by cold SSTAs in the western North Pacific (0°-20°N, 120°-160°E) in the ASMOP-PC3 experiment from April to May, respectively. Similar SSTAs were nudged in the two oceanic domains from April to May in the PACEMAKER category. File Description <ol> <li>Output files were saved in the NetCDF format.</li> <li>The "AGCM.tar.gz" and "PACEMAKER.tar.gz" was the compressed files of the atmospheric outputs in May in the AGCM and PACEMAKER experiments, respectively.</li> <li>In each compressed file, the results of the control run (in the subfolder named "Control_Run"), ASMOP-PC2 (in the subfolder named "ASMOP-PC2"), and ASMOP-PC3 (in the subfolder named "ASMOP-PC3") sensitivity experiments were included for comparison.</li> </ol>

openother-openSep 2022View details →

Dataset used for paper "Evaluating simplifications of subsurface process representations for field-scale permafrost hydrology models"

No description provided.

Processed data for the paper "Evaluating natural language processing models with generalization metrics that do not need access to any training or testing data"

This is the data used to reproduce the results from "Evaluating natural language processing models with generalization metrics that do not need access to any training or testing data".

Scatter plots for the paper "Evaluating natural language processing models with generalization metrics that do not need access to any training or testing data"

This file contains the test-score-vs-metric plots generated by the paper "Evaluating natural language processing models with generalization metrics that do not need access to any training or testing data".

Generalization metrics for the paper "Evaluating natural language processing models with generalization metrics that do not need access to any training or testing data"

This file contains all the generalization metrics that can be used to reproduce the results of "Evaluating natural language processing models with generalization metrics that do not need access to any training or testing data".

Rank correlation results for the paper "Evaluating natural language processing models with generalization metrics that do not need access to any training or testing data"

This file contains the rank correlation results from the paper "Evaluating natural language processing models with generalization metrics that do not need access to any training or testing data".

opencc-zeroOct 2022View details →

dryad32/100

Timing of blood sample processing affects the transcriptomic and epigenomic profiles in CD4+ T-cells of atopic subjects

Optimal pre-analytical conditions for blood sample processing and isolation of selected cell populations for subsequent transcriptomic and epigenomic studies are required to obtain robust and reproducible results. This pilot study was conducted to investigate the potential effects of timing of CD4+ T-cell processing from peripheral blood of atopic and non-atopic adults on their transcriptomic and epigenetic profiles. Two heparinized blood samples were drawn from each of three atopic and three healthy individuals. For each individual, CD4+ T-cells were isolated from the first blood sample within 2 hours (immediate) or from the second blood sample after 24 hours storage (delayed). RNA sequencing (RNA-Seq) and histone H3K27 acetylation chromatin immunoprecipitation sequencing (ChIP-Seq) analyses were performed. A multiplicity of genes was shown to be differentially expressed in immediately processed CD4+ T-cells from atopic versus healthy subjects. These differences disappeared when comparing delayed processed cells due to a drastic change in expression levels of atopy-related genes in delayed processed CD4+ T-cells from atopic donors. This finding was further validated on the epigenomic level by examining H3K27 acetylation profiles. In contrast, transcriptomic and epigenomic profiles of blood CD4+ T-cells of healthy donors remained rather unaffected. Taken together, for successful transcriptomics and epigenomics studies, detailed standard operating procedures developed on the basis of samples from both healthy and disease conditions are implicitly recommended.

opencc-by-3.0-usOct 2022View details →

Spatial snapshots of amyloid precursor protein intramembrane processing via early endosome proteomics

Source datasets for Western blot quantification in Figures S1d and S5a for Park et al. "Spatial snapshots of amyloid precursor protein intramembrane processing via early endosome proteomics".

Dataset and results for "Comparing machine learning and deep learning models for probabilistic post-processing of satellite precipitation-driven streamflow simulation"

Dataset and results for "Comparing machine learning and deep learning models for probabilistic post-processing of satellite precipitation-driven streamflow simulation" Yuhang Zhang1, Aizhong Ye1*, Phu Nguyen2, Bita Analui2, Soroosh Sorooshian2, Kuolin Hsu2 1 State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China. 2 Center for Hydrometeorology and Remote Sensing, Department of Civil and Environmental Engineering, University of California, Irvine, Irvine, California, CA 92697, USA. ## Dataset     Streamflow simulations from one observed precipitation (CMA) and three satellite precipitation products (PDIR, IMERG-F, and GSMaP) for 522 sub-basins. - Q-CMA (streamflow reference) - Q-PDIR (uncorrected) - Q-IMERGF (uncorrected) - Q-GSMAP (uncorrected) ### Data structure - Head section (row1-row5)   - SubNO:    522    - BeginT:    2003-01-01 00:00    - EndT:    2019-12-31 00:00    - Interval:    1440s (daily)   - Revise:    10 (scaling factor to keep int datatype)   - Point1    Point2    ... (Subbasin No.) - Data section   - 6209 rows, 522 cols ## Results Two post-processing model results for test period (2015-1-1 to 2018-12-31). ### Data structure - 1462 rows, every row denotes each day from 2015-1-1 to 2018-12-31 - 100 columns, every column denotes each quantile from 0.005 to 0.995, total 100 quantiles. ### qrf-output - pdir (single input) - imergf (single input) - gsmap (single input) - all (multiple inputs) ### lstm-output - pdir (single input) - imergf (single input) - gsmap (single input) - all (multiple inputs)